Curve　and　surface　reconstruction　methods　play　an　important　role　in　many　research　and　engineering　fields.　It　is　an　imperative　procedure　to　carry　out　surface　reconstruction　from　measurement　data　in　reverse　engineering,　which　is　complicated　with　the　presence　of　outliers.　To　achieve　better　accuracy　and　robustness　of　reconstruction,　an　improved　moving　total　least　squares　(MTLS)　algorithm　based　on　k-means　clustering　called　a　KMTLS　method　is　proposed　in　this　article.　Based　on　MTLS,　KMTLS　adjusts　the　weight　of　discrete　points　within　the　support　domain　by　adopting　a　two-step　fitting　procedure.　Firstly,　an　ordinary　least　squares　(OLS)　method　is　adopted　to　obtain　the　pre-fitting　result　and　calculate　the　residuals　as　the　input　of　k-means　clustering.　In　k-means　clustering,　abnormal　nodes　are　classified　into　one　cluster　and　a　weight　function　based　on　clustering　information　is　introduced　to　deal　with　these　nodes.　Secondly,　based　on　the　compact　weight　function　in　MTLS　and　the　weight　obtained　in　the　pre-fitting　procedure,　a　weighted　total　least　squares　method　is　conducted　to　determine　the　final　estimated　value.　The　process　of　detecting　outliers　is　automatic　without　setting　threshold　artificially.　The　simulation　and　experiment　show　that　KMTLS　has　great　robustness　and　accuracy.　©　2021　Elsevier　Ltd